Neural-network-based two-loop control of robotic manipulators including actuator dynamics in task space

Abstract

A neural-network-based motion controller in task space is presented in this paper. The proposed controller is addressed as a two-loop cascade control scheme. The outer loop is given by kinematic control in the task space. It provides a joint velocity reference signal to the inner one. The inner loop implements a velocity servo loop at the robot joint level. A radial basis function network (RBFN) is integrated with proportional-integral (PI) control to construct a velocity tracking control scheme for the inner loop. Finally, a prototype technology based control system is designed for a robotic manipulator. The proposed control scheme is applied to the robotic manipulator. Experimental results confirm the validity of the proposed control scheme by comparing it with other control strategies.

Robust Saturated PI Joint Velocity Control for Robot Manipulators

“…Motivated by the hierarchical structure in , model‐based motion controllers in operational space were proposed in . Similarly, other recent results are found in , which show designs of robot controllers based on a low‐level joint velocity control loop.…”

Neural Network Control with Disturbance Observer for Uncertain Robot Manipulator

“…1. It has been proved that RBF neural network can approximate any continuous function with arbitrary precision [9], that is…”

Operational space trajectory tracking control of robot manipulators endowed with a primary controller of synthetic joint velocity

“…Let us notice that the kinematic control concept [30] considers system (19) as the robot model, with control input ω d (t, q).…”

“…The most common secondary loop is designed with the aim of satisfying either the motion or the force control objective. The papers [11][12][13][14][15][16][17][18][19], show designs of robot controllers based on a primary joint velocity and secondary task-based loops.…”